ska_sdp_wflow_pointing_offset.utils module

Util functions for:

1. Constructing katpoint antenna information

2. Constructing Measurement Sets names based on ScanIDs

3. Determining observing frequency band

4. Storing errors and traceback in the config db when running the pipeline in SDP

5. Extracts the values from the dictionary of the fitted parameters and outputs them as a numpy array in degrees.

6. Extracting the index of antenna under test in a two-dish mode scenario

7. Merging the output offsets from a two-dish mode scenario

8. Computing the weighted average of the output offsets for the parallel hands gain or visibility amplitudes

9. Computing a weighted-average of the fitted parameters and their standard deviations from beams fitted to the Stokes I amplitudes or parallel-hands gain amplitudes

10. Converting the dictionary of fitted parameters to a numpy array and to a structured numpy array

11. Generating some parameters required for plotting the Gaussian fits in the two-dish mode scenario

12. Splitting array into chunks and discards the first and last chunks if the number of chunks is greater than 1

13. Extracting the cross-correlation Visibility object of the parallel-hands polarisations

14. Selecting frequency ranges for input data and splitting into chunks

ska_sdp_wflow_pointing_offset.utils.antenna_and_offsets_reorder(offsets)

Re-orders the antenna names and their corresponding fitted parameters in the two-dish scenario to match those in the output HDF5 file

Parameters:

offsets – A dictionary of the weighted-average pointing offsets output by array_data_func.weighted_average()

Returns:

A dictionary of the input offsets with the right antenna names and fitted offsets ordering

ska_sdp_wflow_pointing_offset.utils.average_output_offsets(output_offsets_list)

Computes a weighted average of the output offsets for the parallel hands gain or visibility amplitudes output by array_data_func.fitted_offsets_weighted_average().

Parameters:

output_offsets_list – A list of output offsets.

Returns:

A dictionary of the weighted-average output offsets.

ska_sdp_wflow_pointing_offset.utils.construct_antennas(xyz, diameter, station)

Construct list of katpoint Antenna objects based on telescope configuration information.

Parameters:

• xyz – xyz coordinates of antenna positions in [nants, 3]

• diameter – Diameter of dishes in [nants]

• station – List of the antenna names [nants]

Returns:

a set of katpoint Antenna objects

ska_sdp_wflow_pointing_offset.utils.construct_ms_file_name(data_dir, scan_ids=None)

Construct filenames based on scanID. Assumes one MS per scan ID.

Parameters:

• data_dir – Given directory name containing Measurement Sets

• scan_ids – A list of scan IDs

Returns:

A list of Measurement Sets filenames

ska_sdp_wflow_pointing_offset.utils.convert_dict_to_numpy_array(offsets)

Extracts the values from the dictionary of the fitted parameters and outputs them as a numpy array.

Parameters:

offsets – A dictionary of the weighted-average pointing offsets output by array_data_func.weighted_average()

Returns:

A numpy array of the input offsets

ska_sdp_wflow_pointing_offset.utils.convert_offsets_to_degrees_for_kafka(offsets)

Extracts the values from the dictionary of the fitted parameters and outputs them as a numpy array in degrees.

Parameters:

offsets – A dictionary of the weighted-average pointing offsets output by array_data_func.weighted_average()

Returns:

A converted numpy array where all the input offsets are now in degrees except the fitted heights and its uncertainty are in arbitrary units

ska_sdp_wflow_pointing_offset.utils.convert_to_structured(poffset_data, last_scan_index)

Convert pointing offset data from a numpy structure of 13 elements (antenna_name plus 12 floats representing fitting values) to a numpy structured array. The names of the fields are taken to match those in array_data_func.weighted_average() Include the last scan index as the second element in this structured data

Parameters:

• poffset_data – Offsets as numpy column_data

• last_scan_index – Index of last scan

Returns:

Offsets and last_scan_index as structured numpy data

ska_sdp_wflow_pointing_offset.utils.create_dict_for_output_offsets(nants)

Creates a dictionary of the fitted parameters and populates them with NaNs

Parameters:

nants – The number of antennas

Returns:

A dictionary of the fitted parameters populated with NaNs

ska_sdp_wflow_pointing_offset.utils.create_empty_array(antennas)

Create an empty array with 13 columns and as many rows as there are receptors to send to kafka if there is an exception while processing scans

Parameters:

antennas – list of antenna names

Returns:

empty array to send to kafka

ska_sdp_wflow_pointing_offset.utils.get_crosscorrelations(vis)

Extracts the cross-correlation visibilities and their weights from visibilities containing both auto and cross-correlations

Parameters:

vis – Visibility object containing both auto and cross- correlations

Returns:

Cross-correlation Visibility object for the parallel-hands polarisations

ska_sdp_wflow_pointing_offset.utils.get_off_source_antenna_index(on_sky_offsets)

Extracts the index of the antenna under test i.e the antenna performing the five-point scan or similar from the on-sky offsets in a two-dish mode reference pointing observation

Parameters:

on_sky_offsets – The commanded pointings relative to the target in the reference pointing observation in xEl-El coordinates with dimensions (nscans, nants, 2)

Returns:

A list of the index of the antenna performing the five-point or similar scan

ska_sdp_wflow_pointing_offset.utils.get_parameters_for_gaussian_fits_plot(x_per_scan_list, y_per_scan_list, fitted_beams_list)

Generate some parameters required for plotting the Gaussian fits in the two-dish mode scenario i.e, when the beams are fitted to the visibility amplitudes by merging the parameters from the two sets of observations

Parameters:

• x_per_scan_list – List of on-sky offsets from the two sets of observations

• y_per_scan_list – List of visibility amplitudes from the two sets of observations

• fitted_beams_list – The list of fitted beams from the two sets of observations

Returns:

The on-sky offsets with dimension (nscans, nants, 2), the vis amplitudes with dimensions (nants, nscans) when num_chunks=1 or (nants, num_chunks, nscans) when num_chunks > 1. Note that nants is always 2 because only two antennas are used in the observation when the beams are fitted to the visibility amplitudes

ska_sdp_wflow_pointing_offset.utils.handle_exception(err, traceback_frames, scan_ids)

When an exception occurs store the error and traceback in the config db

Parameters:

• err – Exception thrown

• traceback_frames – Exception traceback object

• scan_ids – list of processed scan IDs

Returns:

dict of error information

ska_sdp_wflow_pointing_offset.utils.merge_output_offsets(output_offsets_list)

Merges the output offsets from a two-dish scenario observation into a single dictionary of the output offsets

Parameters:

output_offsets_list – A list of output offsets

Returns:

A dictionary of the merged output offsets

ska_sdp_wflow_pointing_offset.utils.observing_band(frequency_per_chunk)

Compares the frequency per chunk to the dedicated frequency bands for the SKA and returns which observing band was used.

The observing frequency bands were obtained from https://www.skao.int/en/science-users/118/ska-telescope-specifications#__otpm4

Parameters:

frequency_per_chunk – 1D array of the averaged-frequency in each chunk in Hz [num_chunks]

Returns:

The frequency band within which an observation falls

ska_sdp_wflow_pointing_offset.utils.select_channels(freqs, channels, start_freq, end_freq)

Select the desired frequencies and corresponding channels of interest, by inputting the start and end frequency. The function will select the channels between these two frequencies.

Parameters:

• freqs – 1D frequency array in MHz [nchan]

• channels – 1D frequency channel numbers

• start_freq – Start frequency in MHz (float)

• end_freq – End frequency in MHz (float)

Returns:

selected array of (frequencies in MHz, channel numbers)

ska_sdp_wflow_pointing_offset.utils.select_channels_and_split(freqs, channels, start_freq, end_freq, num_chunks)

Selects frequencies and corresponding channels and split them into chunks

Parameters:

• freqs – Frequency array in MHz

• channels – Channel array with same length as freqs

• start_freq – Start frequency in MHz

• end_freq – End frequency in MHz

Returns:

Optionally selected frequencies and channels split into chunks

ska_sdp_wflow_pointing_offset.utils.split_array_into_chunks(array, num_chunks)

Split an array into chunks of num_chunks elements. When num_chunks > 1, the first and last chunks are discarded

Parameters:

• array – Array to be split (equally or unequally) into chunks

• num_chunks – Number of chunks

Returns:

Array with chunks of size num_chunks

ska_sdp_wflow_pointing_offset.utils.weighted_avg_and_std(data, axis, weights, std_error=False)

Compute weighted-average and standard deviation (or error).

Parameters:

• data – Data to be averaged (masked entries are ignored).

• weights – Array of weights with same shape as data along the specified axes.

• axis – Axis or axes along which to average data.

• std_error – If set then return standard error instead of standard deviation (default=False)

Returns:

weighted average and weighted standard deviation (or error).